Why Voracity Is Better


Consider Key Advantages and Differentiators

Ease

 

Voracity's graphical IDE (IRI Workbench) -- as well as its primary 4GL (SortCL) metadata for data definition and management -- is far easier to learn, use, and maintain than most ETL tools, Hadoop code, SQL and shell scripts, and 3GL programs. Voracity in fact addresses the complexity problem across multiple dimensions:

Data Complexity

There are now many more data sources and data types that typical users must engage than ever before, including cloud platforms (e.g., SalesForce, Amazon, Azure) which involve hundreds of sources and end-points. If Voracity connects, it supports. It can even extract and give structure to values in unstructured files.

Large enterprises have organizational complexity and need tools they can share with -- and use to drive consistent results across -- teams. While some tools make it hard to collaborate, Voracity has its data connections and specifications in the same place. Readily available metadata management hubs like Git allow teams to share, secure, track, and control that complexity in a unified way.

Metadata Complexity

Voracity's simple common, 4GLmetadata -- and familiar GUI supporting it -- shrink learning curves and use existing skill sets (e.g., Eclipse, JCL, or SQL), and preclude the need to learn new ones like MapReduce, Teradata ILM, NoSQL, or a legacy ETL tool with multiple steps, much less multiple UIs.

That's especially valuable for IT teams with "lumpy" skills (where some know more than others about certain things), or in smaller resource-constrained SMB environments that cannot manage multiple metadata infrastructures. Voracity allows everyone to auto-enter and standardize on a common metadata framework for data discovery, integration, migration, governance, and analytics.

Business Rule Complexity

Sometimes business logic is complex and/or different across teams. Voracity addresses this with custom transformation, cleansing, masking, and test data generation rules -- and rule categories -- that can be assigned to projects or reached globally, and team-controlled.

Temporal Complexity

The velocity, volume and variety of data are complex, but the speed of data is variable. Not everything is easily scheduled or batched and you may not know when the data will be there.

Voracity runs batch operations through CoSort, MapReduce, or Tez engines, and handles real-time data through brokers like MQTT and Kafka, plus pipes, and Storm. It can also address the variability of data in motion through custom input and output procedures or calling applications that define the pace of data flow.

Ergonomic Complexity

Voracity also has the largest number of job design choices of any ETL tool -- all in the same GUI -- with great detail paid to each:

  1. See the self-documenting ease and openness of IRI DDF and SortCL-based metadata within XML workflows (which also support FACT, Java, SQL, CLI, and DB loader config code).
  2. If you don't want to code in (or outside the GUI), use script-generating end-to-end job creation wizards for ETL, data migration, data masking, metadata, and test data creation. Dynamically-linked script outlines and GUI dialog editors facilitate visual parameter modification.
  3. Or, use the modern Sirius visualization of IRI's workflow and transform mapping diagrams and interactive property screens to design and modify jobs.
  4. Or, if you prefer spreadsheet-style ETL design, use Erwin (AnalytiXDS) Mapping Manager (EMM). Voracity is the only Erwin-supported ETL tool with a live connection to EMM job design. In SSIS and Informatica, for example, you have to export the ETL structures (jobs/mappings) and import each manually.

In IRI Voracity, you can see and use EMM flow design results immediately and use the metadata in the new environment, another competitive design advantage over other ETL tools. An EMM web service is even available to move metadata to and from Voracity for instantaneous interchangeability and sharing of job design metadata.